{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4d5cde8d-6e31-4135-9d83-0801a02cfe4d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as  pd\n",
    "import toad as td \n",
    "pd.set_option('display.max_columns',None)\n",
    "pd.set_option('display.max_rows',600)\n",
    "pd.set_option('display.width', 1000)\n",
    "pd.set_option('display.max_colwidth', None)\n",
    "path1 = './模型结果/dfo.csv'\n",
    "path2 = './模型结果2022/dfo.csv'\n",
    "path3 = './模型结果2023/dfo.csv'\n",
    "df1 = pd.read_csv(path1)\n",
    "df2 = pd.read_csv(path2)\n",
    "df3 = pd.read_csv(path3)\n",
    "df = pd.concat([df1,df2,df3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "90b9b5aa-c19e-4601-b03d-de469c7c3e91",
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "     hjcfzyzlx  hjcfcs    hjcfje  sfblwxtcqy  pwxqzgcs  pwxkzsfzyxqn  hjfxdj  tpfl  hjxypjdj  hjwrfmyq  yjjpjx  sfblwzmqdqy  sfblwqjscqy  wghdbtcflx  wghdbtcfcs  wghdbtcfje  tdlycflx  tdlycfcs   tdlycfje  sfwlszzqy  fxlz  zyjkcflx  zyjkcfcs  zyjkcfje  lgcflx  lgcfcs    lgcfje  sdldjfbscs  nxggzb  sbsffsqjf  fsaqscsgyzcd  aqjglx  aqjgcs    aqjgje  xfaqcflx  xfaqcfcs  xfaqcfje  cpzlcflx  cpzlcfcs   cpzlcfje  yyzzqsl  jscxlxqy  cycsjzdgyhd  mjjdbscs  czgqcs  gqdjzrcs  nsxydj  swcflx  swcfcs     swcfje  swfzch  sfqs  syhljbzdjzcflx  syhljbzdjzcfcs  syhljbzdjzcfje  ldcflx  ldcfcs       ldcfje  jyyccs  jyyclx  yzwfsx  wzjycfje  wzjycfcs  wzjycflx  dcdycs  bdcdycs  dwdbcs  lrsxbzx   qtxzcfje  qtxzcfcs  qtxzcflx  qtajbscs  jnjshbqy  hjxgrz\n",
       "max        3.0     7.0  490000.0         1.0       2.0           1.0     4.0   0.0       7.0       1.0    10.0          1.0          1.0         3.0         2.0     42465.5       3.0       8.0  4500407.0        1.0   NaN       2.0       1.0  125000.0     3.0    11.0  674253.0       103.0   100.0        1.0           NaN     3.0     5.0  190000.0       3.0       6.0  150000.0       3.0       4.0  1635233.0     93.0       1.0          1.0      21.0    50.0      18.0     5.0     3.0     2.0  2312190.0     1.0   1.0             3.0             2.0        250000.0     3.0     1.0  133004400.0     3.0     4.0     1.0  605014.0       4.0       3.0     NaN      NaN    85.0      1.0  7822728.0     279.0       5.0    6464.0       0.0     0.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#先整体（合并）再局部（年份）再细节（模型）\n",
    "\n",
    "df.describe().loc[['max']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "85cb7bc4-ce45-482e-b47f-8fbe9ef4f119",
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "      <td>1</td>\n",
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       "      <td>float64</td>\n",
       "      <td>112686</td>\n",
       "      <td>98.77%</td>\n",
       "      <td>20</td>\n",
       "      <td>1.925234</td>\n",
       "      <td>2.504991</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.10</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gqdjzrcs</th>\n",
       "      <td>float64</td>\n",
       "      <td>112686</td>\n",
       "      <td>98.96%</td>\n",
       "      <td>14</td>\n",
       "      <td>1.708085</td>\n",
       "      <td>1.503226</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>swcfcs</th>\n",
       "      <td>float64</td>\n",
       "      <td>112686</td>\n",
       "      <td>99.99%</td>\n",
       "      <td>2</td>\n",
       "      <td>1.083333</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.89</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>syhljbzdjzcfcs</th>\n",
       "      <td>float64</td>\n",
       "      <td>112686</td>\n",
       "      <td>99.99%</td>\n",
       "      <td>2</td>\n",
       "      <td>1.111111</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.92</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ldcfcs</th>\n",
       "      <td>float64</td>\n",
       "      <td>112686</td>\n",
       "      <td>100.00%</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jyyccs</th>\n",
       "      <td>float64</td>\n",
       "      <td>112686</td>\n",
       "      <td>98.58%</td>\n",
       "      <td>3</td>\n",
       "      <td>1.021290</td>\n",
       "      <td>0.148670</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wzjycfcs</th>\n",
       "      <td>float64</td>\n",
       "      <td>112686</td>\n",
       "      <td>99.97%</td>\n",
       "      <td>4</td>\n",
       "      <td>1.394737</td>\n",
       "      <td>0.916500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dcdycs</th>\n",
       "      <td>float64</td>\n",
       "      <td>112686</td>\n",
       "      <td>100.00%</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bdcdycs</th>\n",
       "      <td>float64</td>\n",
       "      <td>112686</td>\n",
       "      <td>100.00%</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dwdbcs</th>\n",
       "      <td>float64</td>\n",
       "      <td>112686</td>\n",
       "      <td>99.78%</td>\n",
       "      <td>20</td>\n",
       "      <td>4.181452</td>\n",
       "      <td>10.102879</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>8.3</td>\n",
       "      <td>61.01</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>qtxzcfcs</th>\n",
       "      <td>float64</td>\n",
       "      <td>112686</td>\n",
       "      <td>94.31%</td>\n",
       "      <td>62</td>\n",
       "      <td>2.838146</td>\n",
       "      <td>6.938708</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>27.00</td>\n",
       "      <td>279.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>qtajbscs</th>\n",
       "      <td>float64</td>\n",
       "      <td>112686</td>\n",
       "      <td>82.80%</td>\n",
       "      <td>322</td>\n",
       "      <td>13.052775</td>\n",
       "      <td>98.754462</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>17.0</td>\n",
       "      <td>189.51</td>\n",
       "      <td>6464.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   type    size  missing  unique  mean_or_top1  std_or_top2  min_or_top3  1%_or_top4  10%_or_top5  50%_or_bottom5  75%_or_bottom4  90%_or_bottom3  99%_or_bottom2  max_or_bottom1\n",
       "hjcfcs          float64  112686   99.97%       5      1.605263     1.242044          1.0         1.0          1.0             1.0            2.00             3.0            5.89             7.0\n",
       "pwxqzgcs        float64  112686   99.99%       2      1.166667     0.408248          1.0         1.0          1.0             1.0            1.00             1.5            1.95             2.0\n",
       "wghdbtcfcs      float64  112686   99.99%       2      1.142857     0.377964          1.0         1.0          1.0             1.0            1.00             1.4            1.94             2.0\n",
       "tdlycfcs        float64  112686   99.97%       6      1.793103     1.633998          1.0         1.0          1.0             1.0            2.00             4.0            7.16             8.0\n",
       "zyjkcfcs        float64  112686   99.99%       1      1.000000     0.000000          1.0         1.0          1.0             1.0            1.00             1.0            1.00             1.0\n",
       "lgcfcs          float64  112686   99.97%       7      2.361111     2.958308          1.0         1.0          1.0             1.0            1.25             8.0           10.65            11.0\n",
       "sdldjfbscs      float64  112686   96.85%      49      2.750917     5.147570          1.0         1.0          1.0             1.0            2.00             5.0           25.56           103.0\n",
       "fsaqscsgyzcd    float64  112686  100.00%       0           NaN          NaN          NaN         NaN          NaN             NaN             NaN             NaN             NaN             NaN\n",
       "aqjgcs          float64  112686   99.96%       5      1.829268     1.282623          1.0         1.0          1.0             1.0            2.00             4.0            5.00             5.0\n",
       "xfaqcfcs        float64  112686   99.95%       5      1.678571     1.177164          1.0         1.0          1.0             1.0            2.00             3.0            6.00             6.0\n",
       "cpzlcfcs        float64  112686   99.97%       4      1.388889     0.802773          1.0         1.0          1.0             1.0            1.00             3.0            3.65             4.0\n",
       "cycsjzdgyhd     float64  112686   99.94%       1      1.000000     0.000000          1.0         1.0          1.0             1.0            1.00             1.0            1.00             1.0\n",
       "mjjdbscs        float64  112686   99.14%      16      1.821946     1.970081          1.0         1.0          1.0             1.0            2.00             3.0           10.00            21.0\n",
       "czgqcs          float64  112686   98.77%      20      1.925234     2.504991          1.0         1.0          1.0             1.0            2.00             3.0           11.10            50.0\n",
       "gqdjzrcs        float64  112686   98.96%      14      1.708085     1.503226          1.0         1.0          1.0             1.0            2.00             3.0            8.00            18.0\n",
       "swcfcs          float64  112686   99.99%       2      1.083333     0.288675          1.0         1.0          1.0             1.0            1.00             1.0            1.89             2.0\n",
       "syhljbzdjzcfcs  float64  112686   99.99%       2      1.111111     0.333333          1.0         1.0          1.0             1.0            1.00             1.2            1.92             2.0\n",
       "ldcfcs          float64  112686  100.00%       1      1.000000          NaN          1.0         1.0          1.0             1.0            1.00             1.0            1.00             1.0\n",
       "jyyccs          float64  112686   98.58%       3      1.021290     0.148670          1.0         1.0          1.0             1.0            1.00             1.0            2.00             3.0\n",
       "wzjycfcs        float64  112686   99.97%       4      1.394737     0.916500          1.0         1.0          1.0             1.0            1.00             3.0            4.00             4.0\n",
       "dcdycs          float64  112686  100.00%       0           NaN          NaN          NaN         NaN          NaN             NaN             NaN             NaN             NaN             NaN\n",
       "bdcdycs         float64  112686  100.00%       0           NaN          NaN          NaN         NaN          NaN             NaN             NaN             NaN             NaN             NaN\n",
       "dwdbcs          float64  112686   99.78%      20      4.181452    10.102879          1.0         1.0          1.0             1.0            3.00             8.3           61.01            85.0\n",
       "qtxzcfcs        float64  112686   94.31%      62      2.838146     6.938708          1.0         1.0          1.0             1.0            2.00             5.0           27.00           279.0\n",
       "qtajbscs        float64  112686   82.80%     322     13.052775    98.754462          1.0         1.0          1.0             2.0            6.00            17.0          189.51          6464.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst = [col  for col in df.columns if ('cs' in col) ] #or ('je' in col)\n",
    "td.detect(df[lst])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "feec804c-ab63-4b17-a133-3fefe7777de8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import seaborn as sns\n",
    "# import matplotlib.pyplot as plt \n",
    "# sns.displot(df.qtajbscs)\n",
    "# plt.show()\n",
    "df.xycfje.plot(kind='hist',bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "15008d1f-94e1-4a7e-8631-17208975b604",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    1332\n",
       "Name: jscxlxqy, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.qtajbscs.value_counts(bins=100)\n",
    "# df.qtxzcfje.value_counts(bins=40000)\n",
    "\n",
    "\n",
    "\n",
    "df[lst+['custname']].to_csv('./极值分析2.csv',index=False)\n",
    "['sfwlszzqy','jscxlxqy']\n",
    "df.jscxlxqy.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b067edfc-37e5-477a-b0dd-b321cbe7e132",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "528a583a-c9b0-4d0f-89a3-d648bb5c53a6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# help(df.qtajbscs.value_counts)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
